Intelligenza_Artificiale_Lab11_extra_exercises.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1PVWrIhOXik804HcbyqMvWAY1yCnT4bkf","timestamp":1684315813709}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["!pip install scikit-learn\n","from sklearn import datasets\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from sklearn import svm\n","from sklearn.model_selection import cross_val_score\n","from sklearn import metrics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Se0UUmEVDmLj","executionInfo":{"status":"ok","timestamp":1684743075078,"user_tz":-120,"elapsed":8336,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"38340b2e-5c1f-4a24-f3e5-d71bfc65c962"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n"]}]},{"cell_type":"markdown","source":["## EXERCISE 1: Search of the best C\n","\n","Create a SVM with linear kernel using the scikit-learn library. \n","\n","Verify the performance with different values of the hyperparameter C using the function [logspace](https://numpy.org/doc/stable/reference/generated/numpy.logspace.html), nel range -20:0:20. \n","\n","> __QUESTION 1(a):__  Check the best configuration of C, using the 3-fold cross-validation on the digits dataset. \n","\n","> __QUESTION 1(b):__ Plot the score achieved in each configuration, by showing the different values for C in the x-axis and CV score in y-axis. Suggestion: Use the log-scale.\n","\n","> __QUESTION 1(c):__ Train and test the model with the best configuration found at the previous step\n"],"metadata":{"id":"twbLWgpO7YQH"}},{"cell_type":"code","source":["digits_data = datasets.load_digits()\n","#print(digits_data.data)\n","#print(np.unique(digits_data.target))\n","#print(digits_data.target.shape)\n","mean_digits_data={}\n","std_digits_data={}\n","n_samples = len(digits_data.images)\n","data = digits_data.images.reshape((n_samples, -1))\n","for k in np.logspace(-20,0,20):\n","  clf = svm.SVC(kernel='linear', C=k)\n","  scores = cross_val_score(clf, data, digits_data.target, cv=3) # the entire (flatten) dataset\n","  #print(\"C:\",C)\n","  #print(\"Score: \",scores)\n","  #print(\"Mean: \", np.mean(scores))\n","  mean_digits_data[k]=np.mean(scores)\n","  #print(\"Std: \", np.std(scores))\n","  std_digits_data[k]=np.std(scores)\n","#print(std)"],"metadata":{"id":"ORc-FOrDDtZ3","executionInfo":{"status":"ok","timestamp":1684743107244,"user_tz":-120,"elapsed":17406,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["plt.plot(mean_digits_data.keys(),mean_digits_data.values())\n","plt.grid(True)\n","#plt.yscale('log')\n","plt.xscale('log')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":434},"id":"HMwD1mbCsc7g","executionInfo":{"status":"ok","timestamp":1684743567041,"user_tz":-120,"elapsed":1699,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"363fb924-73b1-48cc-ef6d-9dc28e7c1a66"},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["max_c=max(mean_digits_data,key=mean_digits_data.get)\n","print(max_c)\n","print(mean_digits_data.values())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GV50cbE_RabK","executionInfo":{"status":"ok","timestamp":1684743190543,"user_tz":-120,"elapsed":5,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"9dcaefe3-b19e-4f0f-9cdc-6cf3e79c0a6c"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["0.0006951927961775591\n","dict_values([0.16416249304396216, 0.16583194212576516, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.16527545909849747, 0.3789649415692821, 0.9159710628825821, 0.9460211463550361, 0.9449081803005009, 0.9437952142459656, 0.9437952142459656])\n"]}]},{"cell_type":"code","source":["#best is c=10^-17(max(mean,key=mean.get))\n","clf=svm.SVC(kernel='linear', C=max_c)\n","X_train, X_test, y_train, y_test = train_test_split(\n","    data, digits_data.target, test_size=0.25, shuffle=False\n",")\n","\n","clf.fit(X_train, y_train)\n","p=clf.predict(X_test)\n","\n","id = 12\n","plt.gray()\n","plt.matshow(X_test[id].reshape(8,8))\n","plt.show()\n","print(p[id])\n","\n","print(\n","    f\"Classification report for classifier {clf}:\\n\"\n","    f\"{metrics.classification_report(y_test, p)}\\n\"\n",")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":812},"id":"u7UelRuEtEkW","executionInfo":{"status":"ok","timestamp":1684743194510,"user_tz":-120,"elapsed":366,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"d86d53a9-39e3-4fc9-edc0-c7eff324eb9c"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["0\n","Classification report for classifier SVC(C=0.0006951927961775591, kernel='linear'):\n","              precision    recall  f1-score   support\n","\n","           0       0.98      0.98      0.98        43\n","           1       0.96      0.96      0.96        46\n","           2       1.00      1.00      1.00        43\n","           3       0.95      0.77      0.85        47\n","           4       0.96      0.94      0.95        48\n","           5       0.92      0.98      0.95        45\n","           6       0.98      0.98      0.98        47\n","           7       0.90      0.96      0.92        45\n","           8       0.78      0.88      0.83        41\n","           9       0.89      0.87      0.88        45\n","\n","    accuracy                           0.93       450\n","   macro avg       0.93      0.93      0.93       450\n","weighted avg       0.93      0.93      0.93       450\n","\n","\n"]}]},{"cell_type":"markdown","source":["## EXERCISE 2: SVM on the iris_dataset\n","\n","Load the iris dataset from the dataset available at this [link](https://scikit-learn.org/stable/datasets/toy_dataset.html).\n","\n","> __QUESTION 2(a):__  Verify the proprieties (number of samples, number of classes, etc.) of the dataset as we did during the lecture with the digit dataset. \n","\n","> __QUESTION 2(b):__ Split the dataset in training and test according to the following percentages: 70% - 30%\n","\n","> __QUESTION 2(c):__ Train a SVM with different kernel (linear, rbf, polinomial) by leaving the default values for the hyperparameters\n","\n","> __QUESTION 2(d):__ Test the achieved models on the test set\n","\n","> __QUESTION 2(e):__ Represent the performances achieved for each kernel in the form of confusion matrix\n"],"metadata":{"id":"clnOQ355uNMt"}},{"cell_type":"code","source":["iris_ds=datasets.load_iris()\n","#print(iris_ds)\n","\n","print(len(iris_ds.data))\n","print(iris_ds.data.shape)\n","print(np.unique(iris_ds.target))\n","print(iris_ds.target.shape)\n","\n","print('--------')\n","print(iris_ds.target[0])\n","print(iris_ds.target[49])\n","print(iris_ds.target[51])\n","#dataset=0 0 ....... 0 1 ...... 1 2 ...... 2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mMd34aceuWXf","executionInfo":{"status":"ok","timestamp":1684743463879,"user_tz":-120,"elapsed":4,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"82b73990-feea-41a4-d0e0-0050bfcf1131"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["150\n","(150, 4)\n","[0 1 2]\n","(150,)\n","--------\n","0\n","0\n","1\n"]}]},{"cell_type":"code","source":["data_iris=iris_ds.data.reshape((len(iris_ds.data),-1))"],"metadata":{"id":"-d4GcRyXwkhp","executionInfo":{"status":"ok","timestamp":1684743504096,"user_tz":-120,"elapsed":238,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["x_irisTrain,x_irisTest, y_irisTrain, y_irisTest=train_test_split(data_iris , iris_ds.target,test_size=0.30)\n","\n","iris_linear=svm.SVC(kernel='linear')\n","iris_rbf=svm.SVC(kernel='rbf')\n","iris_polinomial=svm.SVC(kernel='poly')"],"metadata":{"id":"apfMwEBtwE5J","executionInfo":{"status":"ok","timestamp":1684743506081,"user_tz":-120,"elapsed":289,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["iris_linear.fit(x_irisTrain,y_irisTrain)\n","iris_rbf.fit(x_irisTrain,y_irisTrain)\n","iris_polinomial.fit(x_irisTrain,y_irisTrain)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"NUo0Pr6nywf7","executionInfo":{"status":"ok","timestamp":1684743508564,"user_tz":-120,"elapsed":248,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"outputId":"22d089b8-25b3-4417-ff6d-5b17d803c321"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='poly')"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["linear_iris_predicted=iris_linear.predict(x_irisTest)\n","rbf_iris_predicted=iris_rbf.predict(x_irisTest)\n","polinomial_iris_predicted=iris_polinomial.predict(x_irisTest)"],"metadata":{"id":"H5Jxpc2DzLDU","executionInfo":{"status":"ok","timestamp":1684743510391,"user_tz":-120,"elapsed":3,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["disp = metrics.ConfusionMatrixDisplay.from_predictions(y_irisTest, linear_iris_predicted)\n","disp.figure_.suptitle(\"Confusion Matrix linear\")\n","plt.show()"],"metadata":{"id":"8rCBO4wKD3_A","executionInfo":{"status":"ok","timestamp":1684743513182,"user_tz":-120,"elapsed":453,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"colab":{"base_uri":"https://localhost:8080/","height":497},"outputId":"d92477f0-dfab-472f-b028-bcc8a883fa45"},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["disp = metrics.ConfusionMatrixDisplay.from_predictions(y_irisTest, rbf_iris_predicted)\n","disp.figure_.suptitle(\"Confusion Matrix rbf\")\n","plt.show()"],"metadata":{"executionInfo":{"status":"ok","timestamp":1684743517254,"user_tz":-120,"elapsed":326,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"colab":{"base_uri":"https://localhost:8080/","height":497},"outputId":"d0e1dfc3-c4bd-42c0-945e-34bfe8db02f9","id":"nR-jS43zz5F0"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["disp = metrics.ConfusionMatrixDisplay.from_predictions(y_irisTest, polinomial_iris_predicted)\n","disp.figure_.suptitle(\"Confusion Matrix polinomial\")\n","plt.show()"],"metadata":{"executionInfo":{"status":"ok","timestamp":1684743521421,"user_tz":-120,"elapsed":392,"user":{"displayName":"Michele Sprocatti","userId":"03401605544328529212"}},"colab":{"base_uri":"https://localhost:8080/","height":497},"outputId":"40944a1b-4383-4c6c-ef47-33cae3fbd7d1","id":"ikpaSW-Fz_pd"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}